Junior Data Analyst

3 days ago


Kuala Lumpur, Kuala Lumpur, Malaysia Love Full time
About Love
We pride ourselves as the best and largest vertically integrated, omni-channel women's fashion brand in the region. Founded in 2010, we have grown to 250 people strong, proudly headquartered in Singapore with country offices in Indonesia and Malaysia and an omni-channel presence across these 3 markets. In addition to our retail franchise in Cambodia, we ship internationally to 15 markets (Hong Kong, China, Philippines and Australia, New Zealand, US, Canada, Macau, Japan, Korea, Vietnam, Thailand, Myanmar, Cambodia & Brunei).

About the Role
We are looking for a Junior Data Engineer to join our team and help build, maintain, and optimize our data pipelines. You will work closely with data analysts, data scientists, and software engineers to ensure efficient data flow, storage, and transformation. This role is ideal for someone passionate about data infrastructure, ETL pipelines, and cloud technologies who wants to grow their skills in a fast-paced environment.

Key Accountabilities
  • Develop and maintain ETL pipelines for efficient data processing and integration.
  • Ensure data quality, reliability, and integrity throughout all data processes.
  • Create and maintain data models that support analytics, reporting, and machine learning needs.
  • Support the development of RESTful APIs for ML model serving.
  • Collaborate with data analysts, scientists, and relevant business stakeholders to understand data requirements and deliver scalable solutions.
  • Maintain data security, governance, and compliance standards.
  • Support visualization tools (Tableau, Metabase).
  • Troubleshoot pipeline failures and implement preventative measures.
  • Document comprehensive data flows, transformations, and technical processes.
  • Experienced in building dashboards using various tools (Tableau, Metabase).

Critical Success Factors & Key Challenges (KPIs)
  • Data Pipeline SLA Adherence – 99.9%+ uptime target for data services.
  • Data Quality and Accuracy (%) – Ensuring transformation correctness and data integrity.
  • ETL Pipeline Success Rate (%) – Minimizing failed job runs and errors.
  • Resource Efficiency ($) - Optimising runtime costs and processing speed.

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